{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T17:22:51Z","timestamp":1770139371001,"version":"3.49.0"},"reference-count":79,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,4,14]],"date-time":"2020-04-14T00:00:00Z","timestamp":1586822400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Dr. Jose Juan Parcero","award":["GV IT-905-16"],"award-info":[{"award-number":["GV IT-905-16"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Heart diseases are highly ranked among the leading causes of mortality in the world. They have various types including vascular, ischemic, and hypertensive heart disease. A large number of medical features are reported for patients in the Electronic Health Records (EHR) that allow physicians to diagnose and monitor heart disease. We collected a dataset from Medica Norte Hospital in Mexico that includes 800 records and 141 indicators such as age, weight, glucose, blood pressure rate, and clinical symptoms. Distribution of the collected records is very unbalanced on the different types of heart disease, where 17% of records have hypertensive heart disease, 16% of records have ischemic heart disease, 7% of records have mixed heart disease, and 8% of records have valvular heart disease. Herein, we propose an ensemble-learning framework of different neural network models, and a method of aggregating random under-sampling. To improve the performance of the classification algorithms, we implement a data preprocessing step with features selection. Experiments were conducted with unidirectional and bidirectional neural network models and results showed that an ensemble classifier with a BiLSTM or BiGRU model with a CNN model had the best classification performance with accuracy and F1-score between 91% and 96% for the different types of heart disease. These results are competitive and promising for heart disease dataset. We showed that ensemble-learning framework based on deep models could overcome the problem of classifying an unbalanced heart disease dataset. Our proposed framework can lead to highly accurate models that are adapted for clinical real data and diagnosis use.<\/jats:p>","DOI":"10.3390\/info11040207","type":"journal-article","created":{"date-parts":[[2020,4,15]],"date-time":"2020-04-15T04:01:46Z","timestamp":1586923306000},"page":"207","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":99,"title":["Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6236-8626","authenticated-orcid":false,"given":"Asma","family":"Baccouche","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, University of Louisville, Louisville, KY 40292, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9356-1186","authenticated-orcid":false,"given":"Begonya","family":"Garcia-Zapirain","sequence":"additional","affiliation":[{"name":"eVida Research Group, University of Deusto, 48007 Bilbao, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8717-7524","authenticated-orcid":false,"given":"Cristian","family":"Castillo Olea","sequence":"additional","affiliation":[{"name":"eVida Research Group, University of Deusto, 48007 Bilbao, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5274-8596","authenticated-orcid":false,"given":"Adel","family":"Elmaghraby","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Louisville, Louisville, KY 40292, USA"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,14]]},"reference":[{"key":"ref_1","first-page":"e38","article-title":"Heart disease and stroke statistics-2016 update a report from the American Heart Association","volume":"133","author":"Mozaffarian","year":"2016","journal-title":"Circulation"},{"key":"ref_2","first-page":"1176","article-title":"Secondary use of EHR: Data quality issues and informatics opportunities","volume":"18","author":"Pariente","year":"2009","journal-title":"Pharmacoepidemiol. Drug Saf."},{"key":"ref_3","first-page":"1176","article-title":"Data mining on electronic health record databases for signal detection in pharmacovigilance: Which events to monitor","volume":"1","author":"Botsis","year":"2010","journal-title":"Summit Transl. Bioinform."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1146\/annurev-publhealth-031914-122747","article-title":"Uses of electronic health records for public health surveillance to advance public health","volume":"36","author":"Birkhead","year":"2015","journal-title":"Annu. Rev. Public Health"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Impedovo, D., Pirlo, G., and Vessio, G. (2018). Dynamic handwriting analysis for supporting earlier Parkinson\u2019s disease diagnosis. Information, 9.","DOI":"10.3390\/info9100247"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"48","DOI":"10.26599\/BDMA.2018.9020031","article-title":"Big data analytics for healthcare industry: Impact, applications, and tools","volume":"2","author":"Kumar","year":"2018","journal-title":"Big Data Min. Anal."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1562","DOI":"10.1109\/TMI.2018.2791721","article-title":"Interactive medical image segmentation using deep learning with image-specific fine tuning","volume":"37","author":"Wang","year":"2018","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"65333","DOI":"10.1109\/ACCESS.2018.2875677","article-title":"Patient2vec: A personalized interpretable deep representation of the longitudinal electronic health record","volume":"6","author":"Zhang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","article-title":"Deep convolutional neural network for computer-aided detection: CNN architectures, dataset characteristics and transfer learning","volume":"35","author":"Shin","year":"2016","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ghoniem, R.M. (2020). A Novel Bio-Inspired Deep Learning Approach for Liver Cancer Diagnosis. Information, 11.","DOI":"10.3390\/info11020080"},{"key":"ref_11","first-page":"1275","article-title":"Prediction of heart disease using machine learning algorithms","volume":"2","author":"Nikhar","year":"2016","journal-title":"Int. J. Adv. Eng. Manag. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"104992","DOI":"10.1016\/j.cmpb.2019.104992","article-title":"A new machine learning technique for an accurate diagnosis of coronary artery disease","volume":"179","author":"Abdar","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.cmpb.2009.01.003","article-title":"Support vectors machine-based identification of heart valve diseases using heart sounds","volume":"95","author":"Maglogiannis","year":"2009","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tjahjadi, H., and Ramli, K. (2020). Noninvasive Blood Pressure Classification Based on Photoplethysmography Using K-Nearest Neighbors Algorithm: A Feasibility Study. Information, 11.","DOI":"10.3390\/info11020093"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1589","DOI":"10.1109\/JBHI.2017.2767063","article-title":"Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis","volume":"22","author":"Shickel","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1093\/bib\/bbx044","article-title":"Deep learning for healthcare: Review, opportunities and challenges","volume":"19","author":"Miotto","year":"2017","journal-title":"Brief. Bioinform."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Rajamhoana, S.P., Devi, C.A., Umamaheswari, K., Kiruba, R., Karunya, K., and Deepika, R. (2018, January 4\u20136). Analysis of neural networks based heart disease prediction system. Proceedings of the 2018 11th International Conference on Human System Interaction (HSI), Gda\u0144sk, Poland.","DOI":"10.1109\/HSI.2018.8431153"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.compbiomed.2017.08.022","article-title":"A deep convolutional neural network model to classify heartbeats","volume":"89","author":"Acharya","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"34717","DOI":"10.1109\/ACCESS.2020.2974687","article-title":"An IoT Framework for Heart Disease Prediction Based on MDCNN Classifier","volume":"8","author":"Khan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Miotto, R., Li, L., and Dudley, J.T. (2016, January 20\u201323). Deep learning to predict patient future diseases from the electronic health records. Proceedings of the European Conference on Information Retrieval, Padua, Italy.","DOI":"10.1007\/978-3-319-30671-1_66"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1093\/jamia\/ocw112","article-title":"Using recurrent neural network models for early detection of heart failure onset","volume":"24","author":"Choi","year":"2016","journal-title":"J. Am. Med Inform. Assoc."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Park, H.D., Han, Y., and Choi, J.H. (2018, January 17\u201319). Frequency-Aware Attention based LSTM Networks for Cardiovascular Disease. Proceedings of the 2018 International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Korea.","DOI":"10.1109\/ICTC.2018.8539509"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Park, S., Kim, Y.J., Kim, J.W., Park, J.J., Ryu, B., and Ha, J.W. (2018, January 29\u201331). [Regular Paper] Interpretable Prediction of Vascular Diseases from Electronic Health Records via Deep Attention Networks. Proceedings of the 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE), Taichung, Taiwan.","DOI":"10.1109\/BIBE.2018.00028"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1109\/TNNLS.2012.2202289","article-title":"Bidirectional extreme learning machine for regression problem and its learning effectiveness","volume":"23","author":"Yang","year":"2012","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Fei, H., and Tan, F. (2018). Bidirectional grid long short-term memory (bigridlstm): A method to address context-sensitivity and vanishing gradient. Algorithms, 11.","DOI":"10.3390\/a11110172"},{"key":"ref_26","unstructured":"Wang, P., Qian, Y., Soong, F.K., He, L., and Zhao, H. (2015). A unified tagging solution: Bidirectional lstm recurrent neural network with word embedding. arXiv, Available online: www.arxiv.org\/abs\/1511.00215."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Jagannatha, A.N., and Yu, H. (2016, January 7\u201312). Bidirectional RNN for medical event detection in electronic health records. Proceedings of the Conference Association for Computational Linguistics, Berlin, Germany.","DOI":"10.18653\/v1\/N16-1056"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"102119","DOI":"10.1109\/ACCESS.2019.2931500","article-title":"Automatic cardiac arrhythmia classification using combination of deep residual network and bidirectional LSTM","volume":"7","author":"He","year":"2019","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"67927","DOI":"10.1109\/ACCESS.2018.2879158","article-title":"Deep Feature Learning for Disease Risk Assessment Based on Convolutional Neural Network With Intra-Layer Recurrent Connection by Using Hospital Big Data","volume":"6","author":"Usama","year":"2018","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.bspc.2017.12.004","article-title":"Classification of imbalanced ECG beats using re-sampling techniques and AdaBoost ensemble classifier","volume":"41","author":"Rajesh","year":"2018","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Esfahani, H.A., and Ghazanfari, M. (2017, January 22). Cardiovascular disease detection using a new ensemble classifier. Proceedings of the 2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI), Tehran, Iran.","DOI":"10.1109\/KBEI.2017.8324946"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Pasanisi, S., and Paiano, R. (2018). A hybrid information mining approach for knowledge discovery in cardiovascular disease (CVD). Information, 9.","DOI":"10.3390\/info9040090"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zabihi, M., Rad, A.B., Kiranyaz, S., Gabbouj, M., and Katsaggelos, A.K. (2016, January 11\u201314). Heart sound anomaly and quality detection using ensemble of neural network without segmentation. Proceedings of the 2016 Computing in Cardiology Conference (CinC), Vancouver, BC, Canada.","DOI":"10.22489\/CinC.2016.180-213"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4263","DOI":"10.1109\/TCYB.2016.2606104","article-title":"A noise-filtered under-sampling scheme for imbalanced classification","volume":"47","author":"Kang","year":"2016","journal-title":"IEEE Trans. Cybern."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1356","DOI":"10.1109\/TKDE.2014.2345380","article-title":"Resampling-based ensemble methods for online class imbalance learning","volume":"27","author":"Wang","year":"2014","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.cmpb.2018.03.009","article-title":"Electronic health record with computerized decision support tools for the purposes of a pediatric cardiovascular heart disease screening program in Crete","volume":"159","author":"Chatzakis","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sowmiya, C., and Sumitra, P. (2017, January 23\u201325). Analytical study of heart disease diagnosis using classification techniques. Proceedings of the 2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), Tamilnadu, India.","DOI":"10.1109\/ITCOSP.2017.8303115"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"81542","DOI":"10.1109\/ACCESS.2019.2923707","article-title":"Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques","volume":"7","author":"Mohan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"S106","DOI":"10.1097\/MLR.0b013e3181de9e17","article-title":"Prediction modeling using EHR data: Challenges, strategies, and a comparison of machine learning approaches","volume":"48","author":"Wu","year":"2010","journal-title":"Med. Care"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1109\/TBME.2018.2877649","article-title":"Magnetocardiography-Based Ischemic Heart Disease Detection and Localization Using Machine Learning Methods","volume":"66","author":"Tao","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.cmpb.2018.07.002","article-title":"Cardiology record multi-label classification using latent Dirichlet allocation","volume":"164","author":"Casillas","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.cmpb.2017.01.004","article-title":"Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm","volume":"141","author":"Arabasadi","year":"2017","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Kumar, V., and Garg, M.L. (2017, January 17\u201318). Deep learning in predictive analytics: A survey. Proceedings of the 2017 International Conference on Emerging Trends in Computing and Communication Technologies (ICETCCT), Dehradun, India.","DOI":"10.1109\/ICETCCT.2017.8280331"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.jbi.2016.01.009","article-title":"Developing EHR-driven heart failure risk prediction models using CPXR (Log) with the probabilistic loss function","volume":"60","author":"Taslimitehrani","year":"2016","journal-title":"J. Biomed. Inform."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1109\/RBME.2018.2885714","article-title":"Deep Learning in Cardiology","volume":"12","author":"Bizopoulos","year":"2018","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Hsiao, H.C., Chen, S.H., and Tsai, J.J. (November, January 31). Deep learning for risk analysis of specific cardiovascular diseases using environmental data and outpatient records. Proceedings of the 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE), Taichung, Taiwan.","DOI":"10.1109\/BIBE.2016.75"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"4379","DOI":"10.1007\/s11042-017-5515-y","article-title":"Hybrid recommendation system for heart disease diagnosis based on multiple kernel learning with adaptive neuro-fuzzy inference system","volume":"77","author":"Manogaran","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1394","DOI":"10.1109\/JIOT.2018.2845128","article-title":"Automatic classification of fetal heart rate based on convolutional neural network","volume":"6","author":"Li","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JTEHM.2019.2907945","article-title":"Deep Learning-Based Proarrhythmia Analysis Using Field Potentials Recorded From Human Pluripotent Stem Cells Derived Cardiomyocytes","volume":"7","author":"Golgooni","year":"2019","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Maknickas, V., and Maknickas, A. (2017, January 24\u201327). Atrial fibrillation classification using qrs complex features and lstm. Proceedings of the 2017 Computing in Cardiology (CinC), Rennes, France.","DOI":"10.22489\/CinC.2017.350-114"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Grzegorczyk, I., Soli\u0144ski, M., \u0141epek, M., Perka, A., Rosi\u0144ski, J., Rymko, J., and Giera\u0142towski, J. (2016, January 11\u201314). PCG classification using a neural network approach. Proceedings of the 2016 Computing in Cardiology Conference (CinC), Vancouver, BC, Canada.","DOI":"10.22489\/CinC.2016.323-252"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.compbiomed.2018.06.026","article-title":"A study of time-frequency features for CNN-based automatic heart sound classification for pathology detection","volume":"100","author":"Bozkurt","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"109870","DOI":"10.1109\/ACCESS.2019.2933473","article-title":"Interpretability Analysis of Heartbeat Classification Based on Heartbeat Activity\u2019s Global Sequence Features and BiLSTM-Attention Neural Network","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"570","DOI":"10.3348\/kjr.2017.18.4.570","article-title":"Deep learning in medical imaging: General overview","volume":"18","author":"Lee","year":"2017","journal-title":"Korean J. Radiol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1021\/acs.molpharmaceut.5b00982","article-title":"Applications of deep learning in biomedicine","volume":"13","author":"Mamoshina","year":"2016","journal-title":"Mol. Pharm."},{"key":"ref_56","first-page":"1","article-title":"Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network","volume":"9","author":"Zhu","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Li, L.J., Niu, C.Q., Pu, D.X., and Jin, X.Y. (2018, January 19\u201321). Electronic Medical Data Analysis Based on Word Vector and Deep Learning Model. Proceedings of the 2018 9th International Conference on Information Technology in Medicine and Education (ITME), Hangzhou, China.","DOI":"10.1109\/ITME.2018.00114"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Chen, C.W., Tseng, S.P., Kuan, T.W., and Wang, J.F. (2020). Outpatient Text Classification Using Attention-Based Bidirectional LSTM for Robot-Assisted Servicing in Hospital. Information, 11.","DOI":"10.3390\/info11020106"},{"key":"ref_59","first-page":"30","article-title":"Diagnosing coronary heart disease using ensemble machine learning","volume":"7","author":"Miao","year":"2016","journal-title":"Int. J. Adv. Comput. Sci. Appl. (IJACSA)"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Yekkala, I., Dixit, S., and Jabbar, M.A. (2017, January 17\u201319). Prediction of heart disease using ensemble learning and Particle Swarm Optimization. Proceedings of the 2017 International Conference on Smart Technologies for Smart Nation (SmartTechCon), Bengaluru, India.","DOI":"10.1109\/SmartTechCon.2017.8358460"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"7675","DOI":"10.1016\/j.eswa.2008.09.013","article-title":"Effective diagnosis of heart disease through neural network ensembles","volume":"36","author":"Das","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.cmpb.2008.09.005","article-title":"Diagnosis of valvular heart disease through neural network ensembles","volume":"93","author":"Das","year":"2009","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"69559","DOI":"10.1109\/ACCESS.2019.2912226","article-title":"Deep Ensemble Detection of Congestive Heart Failure using Short-term RR Intervals","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.cmpb.2016.09.003","article-title":"A new approach to early diagnosis of congestive heart failure disease by using Hilbert\u2013Huang transform","volume":"137","author":"Altan","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1145\/1007730.1007735","article-title":"A study of the behavior of several methods for balancing machine learning training data","volume":"6","author":"Batista","year":"2004","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Wosiak, A., and Karbowiak, S. (2017, January 3\u20136). Preprocessing compensation techniques for improved classification of imbalanced medical datasets. Proceedings of the 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), Prague, Czech Republic.","DOI":"10.15439\/2017F82"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Ge, H., Sun, K., Sun, L., Zhao, M., and Wu, C. (2018, January 3\u20136). A selective ensemble learning framework for ECG-based heartbeat classification with imbalanced data. Proceedings of the 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain.","DOI":"10.1109\/BIBM.2018.8621523"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_69","unstructured":"Jain, A., Zamir, A.R., Savarese, S., and Saxena, A. (July, January 26). Structural-RNN: Deep learning on spatio-temporal graphs. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_71","unstructured":"Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2015, January 6\u201311). Gated feedback recurrent neural network. Proceedings of the 2015 International Conference on Machine Learning, Lille, France."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Dal Pozzolo, A., Caelen, O., Johnson, R.A., and Bontempi, G. (2015, January 8\u201310). Calibrating probability with undersampling for unbalanced classification. Proceedings of the 2015 IEEE Symposium Series on Computational Intelligence, Cape Town, South Africa.","DOI":"10.1109\/SSCI.2015.33"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1613\/jair.1.11192","article-title":"SMOTE for learning from imbalanced data: Progress and challenges, marking the 15-year anniversary","volume":"61","author":"Garcia","year":"2018","journal-title":"J. Artif. Intell. Res."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.patrec.2017.05.019","article-title":"A new image classification method based on modified condensed nearest neighbor and convolutional neural network","volume":"94","author":"Liang","year":"2017","journal-title":"Pattern Recognit. Lett."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Yu, Y., Lin, H., Meng, J., Wei, X., and Zhao, Z. (2017). Assembling deep neural networks for medical compound figure detection. Information, 8.","DOI":"10.3390\/info8020048"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1093\/protein\/13.1.15","article-title":"Is it better to combine predictions?","volume":"13","author":"King","year":"2000","journal-title":"Protein Eng."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Zeng, Z.Y., Lin, J.J., Chen, M.S., Chen, M.H., Lan, Y.Q., and Liu, J.L. (2019). A Review Structure Based Ensemble Model for Deceptive Review Spam. Information, 10.","DOI":"10.3390\/info10070243"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.ins.2017.05.008","article-title":"Clustering-based undersampling in class-imbalanced data","volume":"409","author":"Lin","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1016\/j.neucom.2015.04.120","article-title":"Study of the impact of resampling methods for contrast pattern based classifiers in imbalanced databases","volume":"175","year":"2016","journal-title":"Neurocomputing"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/4\/207\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:21:07Z","timestamp":1760361667000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/4\/207"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,14]]},"references-count":79,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["info11040207"],"URL":"https:\/\/doi.org\/10.3390\/info11040207","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,14]]}}}